CN112383065A - Distributed MPC-based power distribution network dynamic voltage control method - Google Patents

Distributed MPC-based power distribution network dynamic voltage control method Download PDF

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CN112383065A
CN112383065A CN202011174798.3A CN202011174798A CN112383065A CN 112383065 A CN112383065 A CN 112383065A CN 202011174798 A CN202011174798 A CN 202011174798A CN 112383065 A CN112383065 A CN 112383065A
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distribution network
distributed
energy storage
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power
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CN112383065B (en
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杨帆
张章
李桂鑫
徐晶
徐科
刘英英
罗涛
迟福建
胡源
王哲
孙阔
夏冬
张梁
张雪菲
崔荣靖
李娟�
祁彦鹏
王世举
李广敏
郑戍洁
杨国朝
张宏艳
刘伟
赵长伟
范朕宁
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State Grid Corp of China SGCC
State Grid Tianjin Electric Power Co Ltd
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State Grid Corp of China SGCC
State Grid Tianjin Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/48—Controlling the sharing of the in-phase component
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/50—Controlling the sharing of the out-of-phase component
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy

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Abstract

The invention provides a power distribution network dynamic voltage control method based on distributed model predictive control, which comprises the following steps: establishing a distributed photovoltaic dynamic mathematical model and discretizing the distributed photovoltaic dynamic mathematical model; establishing a distributed energy storage dynamic mathematical model and discretizing the distributed energy storage dynamic mathematical model; obtaining a distribution network voltage-active reactive power sensitivity matrix based on load flow calculation, decomposing a distribution network model, converting the distribution network model into a weak coupling subsystem model, and solving subsystem model topological parameters; uniformly arranging the distributed photovoltaic, energy storage and power distribution network subsystem models into an integral power distribution network dynamic voltage control model; considering the charge state, the rated capacity and the maximum active output limit of distributed energy storage and the maximum reactive output limit of distributed photovoltaic; calculating a dynamic voltage control instruction of the power distribution network in real time based on a distributed model prediction control method according to the power distribution network control model; the invention can make up the defects of the traditional power distribution network voltage control and solve the voltage problem of grid connection of a large number of distributed power supplies.

Description

Distributed MPC-based power distribution network dynamic voltage control method
Technical Field
The invention relates to the field of new energy power generation of a power system, in particular to a distributed MPC-based power distribution network dynamic voltage control method.
Background
With the access of large-scale distributed photovoltaic to a power distribution network, although an energy utilization structure on the side of the power distribution network is improved, and the application range of new energy is enlarged, the output of the distributed photovoltaic power distribution network has higher fluctuation and randomness, which is the root cause of influence generated by the access of the distributed photovoltaic power to the power distribution network, especially when the access proportion of the distributed photovoltaic power to the power distribution network reaches a certain degree, uncontrollable active output fluctuation of the distributed photovoltaic power brings great influence on the power balance of the power distribution network, and in a medium-low voltage power distribution network with large line resistance, severe fluctuation of the active power can cause problems of out-of-limit voltage, fluctuation and the like to occur frequently. The traditional power distribution network can only adjust the voltage through reactive power adjusting equipment such as a load transformer, a static compensator and the like, but has the defects of slow adjusting process, long action period and the like, and a large-scale distributed power supply is easy to cause a new voltage problem after being connected to the grid. The limitation of the conventional voltage control is continuously enlarged due to the rapid random fluctuation characteristic of the current distributed power supply, so that the significance of researching a dynamic voltage control method of a power distribution network containing distributed energy storage is great, most researches are only limited to the voltage control of a steady-state power flow model due to the lack of the dynamic voltage control method of the power distribution network and the lack of description on the dynamic characteristics of distributed photovoltaic and energy storage, long-time scale optimization algorithms are adopted in the control method, and the consideration on the dynamic requirements of the voltage control is lacked.
From the traditional voltage control method, a steady-state model is obtained based on a power flow calculation method, control instructions of each control device are optimized based on the model, the process is to acquire the global state quantity of a system, and the problem of huge data and complex calculation exists in the solving process, so that much inconvenience is brought to the voltage control of a distributed power supply connected to a power grid. The flexibility and the rapidity of dynamic control are ensured.
Disclosure of Invention
The invention relates to a distribution network dynamic voltage control method based on a distributed MPC (multi-control processor), which comprises the following steps of:
step S1, defining a distributed photovoltaic dynamic mathematical model working in a reactive-voltage droop control mode and discretizing the model;
step S2, establishing a distributed energy storage dynamic mathematical model working in a current source type control mode and discretizing the model;
and step S3, obtaining a distribution network voltage-active reactive power sensitivity matrix based on load flow calculation.
Step S4, decomposing the power distribution network model by using an epsilon decomposition method, converting the power distribution network model into a plurality of weak coupling subsystem models, and solving the topological parameters of the subsystem models through a deep first search algorithm; (ii) a
Step S5, uniformly arranging the distributed photovoltaic, energy storage and power distribution network subsystem models into an overall power distribution network dynamic voltage control model;
step S6, considering the charge state, the rated capacity and the maximum active output limit of the distributed energy storage and the maximum reactive output limit of the distributed photovoltaic, and expressing the charge state, the rated capacity and the maximum active output limit as an inequality constraint form;
and step S7, calculating a real-time power distribution network dynamic voltage control instruction based on a distributed model prediction control method according to the power distribution network dynamic voltage control model.
The distributed photovoltaic dynamic mathematical model in the step 1 is as follows:
the dynamic model of the active power output of the distributed photovoltaic MPPT is
Figure BDA0002748392560000021
Wherein, TpvIs the photovoltaic time constant, PPVTo output active power for the photovoltaic inverter,
Figure BDA0002748392560000022
representing its differential amount, PMPPTOutputting power for photovoltaic MPPT;
the distributed photovoltaic reactive-voltage droop control dynamic model is
Figure BDA0002748392560000023
Wherein, tau1Is a filter constant, QPV,inThe photovoltaic inverter inputs the reactive power,
Figure BDA0002748392560000024
representing its differential amount, QoFor reactive power reference commands, KdIs a reactive-voltage droop coefficient, ViTo grid point voltage, VrefIs a grid-connected point voltage reference value;
the distributed photovoltaic inverter reactive power output dynamic model is
Figure BDA0002748392560000025
Wherein Q isiTo output the reactive power for the inverter,
Figure BDA0002748392560000026
represents the differential amount thereof;
discretizing and writing the model into a state space equation form: x is the number ofPV(k+1)＝APVxPV(k)+BPVuPV(k)+BdPVdPV(k) Wherein x isPV，dPV，uPVFor the state, disturbance and control vectors of distributed photovoltaics, APV，BPV，BdPVIs a distributed photovoltaic system matrix.
The distributed energy storage dynamic mathematical model in the step 2 is as follows:
the distributed energy storage active power output dynamic model comprises the following steps:
Figure BDA0002748392560000027
wherein, tau2pFor storing the active filter constant, PPIThe active power is input to the PI controller,
Figure BDA0002748392560000028
is PPIDifferential amount, PBESSFor the energy-storage converter to output the actual active power, PBESSrefA reference instruction of active power of energy storage;
Figure BDA0002748392560000029
wherein k isp、kiAs a parameter of the PI controller, PBESS，inAn active command is input to the energy storage converter,
Figure BDA0002748392560000031
representing its differential amount, PPIThe active power is input to the PI controller,
Figure BDA0002748392560000032
represents the differential amount thereof;
Figure BDA0002748392560000033
wherein, TBESSFor the time constant of the energy storage converter,
Figure BDA0002748392560000034
and outputting differential quantity of actual active power for the energy storage converter.
The distributed energy storage reactive power output dynamic model comprises the following steps:
Figure BDA0002748392560000035
wherein, tau2pFor the constant, Q, of the energy-storing reactive filterPIThe reactive power is input for the PI-controller,
Figure BDA0002748392560000036
inputting differential amounts of reactive power, Q, for PI controllersBESSFor outputting actual reactive power, Q, of the energy-storing converterBESSrefA reference instruction for energy storage reactive power;
Figure BDA0002748392560000037
wherein k isp、kiFor PI controller parameters, QBESS，inA reactive instruction is input for the energy storage converter,
Figure BDA0002748392560000038
representing its differential amount, QPIThe reactive power is input for the PI-controller,
Figure BDA0002748392560000039
represents the differential amount thereof;
Figure BDA00027483925600000310
wherein, TBESSFor the time constant of the energy storage converter,
Figure BDA00027483925600000311
outputting differential quantity of actual reactive power for the energy storage converter;
an energy storage state of charge model:
Figure BDA00027483925600000312
wherein, SOC (k) and SOC (k +1) are energy storage SOC values at sampling time k and k +1, and PBESS(k) For the actual output of active power, T, of the stored energy at sampling instant ksTo sample time, EmaxThe maximum capacity for energy storage;
discretizing and writing the distributed energy storage dynamic mathematical model into a state space equation form: x is the number ofES(k+1)＝AESxES(k)+BESuES(k) Wherein x isES，uESFor distributed energy storage states and control vectors, AES，BESIs a distributed energy storage system matrix.
The voltage-active reactive power sensitivity matrix of the power distribution network in the step 3 is as follows:
Figure BDA00027483925600000313
wherein ΛθP、ΛθQ、ΛVP、ΛVQRepresenting the voltage sensitivity coefficient.
The subsystem model in the step 4 is as follows:
ΛVP＝Λ′VP+ε·R (2)
wherein, Λ'VPThe sensitivity matrix with elements larger than the epsilon value describes the strong coupling relation of the system, and epsilon.R is a residual error matrix describing the weak coupling of the system. All element values in R are less than or equal to 1. In addition to quantitatively describing the coupling between the distributed power sources and the nodes, the range of influence of each distributed power source is described as well as a new network topology that ignores weak couplings. For matrix Λ'VPOne permutation matrix P would be derived of'VPIs converted into
Figure BDA00027483925600000314
Wherein
Figure BDA00027483925600000315
Is a block diagonal matrix. In that
Figure BDA00027483925600000316
Each blocking matrix represents the sensitivity relationship of each subsystem. By passing
Figure BDA00027483925600000317
The described subnet topology is conveniently obtained by using a depth first search algorithm.
The power distribution network integral model in the step 5 is as follows:
Figure BDA0002748392560000041
in the formula, xi，ui，yiAre respectively a distribution network subsystem SiThe state quantity, the control input quantity and the output quantity, xjFor the sub-system S of the distribution networkjAmount of state of (A)i、Bi、DiFor the sub-system S of the distribution networkiSystem matrix of AijFor the sub-system S of the distribution networkiAnd SjSystem coupling matrix of, NiThe number of subsystems of the power distribution network. If the matrix A isijIf not, then the sub-system and S are representediIs SjCoupled, the two are adjacent systems.
The inequality constraint in the step 6 is as follows:
Figure BDA0002748392560000042
Figure BDA0002748392560000043
Figure BDA0002748392560000044
in the formula, (4) energy storage active output constraint, (5) energy storage reactive output constraint, and (6) photovoltaic reactive output constraint.
And considering the energy storage SOC and the voltage deviation regulation effect, and limiting the control by taking the energy storage SOC and the voltage deviation regulation effect as output constraints:
ΔVmin≤ΔV≤ΔVmax (7)
SOCmin≤SOC≤SOCmax (8)
in the formula, (7) is node voltage deviation constraint, and (8) is energy storage SOC constraint.
Wherein the content of the first and second substances,
Figure BDA0002748392560000045
for the minimum active output value of the stored energy,
Figure BDA0002748392560000046
the maximum active output value is the stored energy;
Figure BDA0002748392560000047
is the minimum idle work output value of the stored energy,
Figure BDA0002748392560000048
the maximum reactive power output value is the stored energy; SOCminTo store the lower limit of the energy SOC, SOCmaxIs the upper limit of the energy storage SOC;
Figure BDA0002748392560000049
is the photovoltaic minimum reactive power output value,
Figure BDA00027483925600000410
the maximum photovoltaic reactive power output value is obtained; Δ VminIs the lower limit of the voltage deviation, Δ VmaxIs the upper limit of the voltage deviation.
The calculation method of the real-time power distribution network dynamic voltage control instruction in the step 7 is as follows:
the future dynamics of the system can be predicted based on the model (3) according to the predictive control philosophy. For this purpose, the system prediction time domain is set to NpControl time domain as NmAnd N ism≤Np. At the current time k, a calculation can be madeΔ x (k) ═ x (k) — x (k-1), and using this as a starting point for predicting the future dynamics of the system, the system state can be predicted from (3) as follows:
Figure BDA00027483925600000411
in the formula (I), the compound is shown in the specification,
Figure BDA0002748392560000051
Figure BDA0002748392560000052
Figure BDA0002748392560000053
Figure BDA0002748392560000054
Figure BDA0002748392560000055
and (3) converting the inequality constraint in the step 6 into the following form through a prediction equation:
Figure BDA0002748392560000056
in the formula, Su,bi，Sx,bi，Sx,bij，Sd,biTo constrain the matrix, Ymax，YminTo output a constraint vector, Δ UiTo control the increments.
The conversion of the problem to QCQP is described as follows:
Figure BDA0002748392560000057
in the formula, CuB (k +1| k) represents a constraint matrix and a vector,
Figure BDA0002748392560000058
and
Figure BDA0002748392560000059
representing a weighting matrix.
The distributed MPC-based power distribution network dynamic voltage control method provided by the invention has the beneficial effects that:
1. the method for controlling the dynamic voltage of the power distribution network based on the distributed MPC considers the dynamic characteristics of distributed photovoltaic and energy storage, and has the advantages of high control precision, high speed, high flexibility and reliability and the like;
2. the control method provided by the invention overcomes the defects of low control speed, complex calculation of related instructions, huge and redundant data, insufficient flexibility of reactive equipment and the like of the traditional control method, provides a new thought for voltage control of the power distribution network with large-scale access of the distributed power supply, brings distributed photovoltaic and energy storage equipment with different characteristics into a voltage control framework of the power distribution network, establishes dynamic characteristics by considering photovoltaic and energy storage of a local working mode, designs a dynamic voltage control method of distributed model predictive control on the basis of the dynamic voltage control method, avoids huge real-time calculation amount, and ensures the flexibility and rapidity of the dynamic control.
Drawings
Fig. 1 is a flowchart of a method for controlling dynamic voltage of a power distribution network based on a distributed MPC according to the present invention;
fig. 2 is a diagram illustrating an effect of the distribution network voltage provided by the embodiment before the control method of the present invention is implemented;
fig. 3 is a diagram illustrating an effect of the distribution network voltage after implementing the control method of the present invention;
Detailed Description
The invention provides a distributed MPC-based power distribution network dynamic voltage control method, the flow of which is shown in FIG. 1. As can be seen from FIG. 1, the method comprises the following steps:
step S1, defining a distributed photovoltaic third-order dynamic mathematical model working in a reactive-voltage droop control mode and discretizing the model;
step S2, establishing a distributed energy storage three-order dynamic mathematical model working in a current source type control mode and discretizing the model;
and step S3, obtaining a distribution network voltage-active reactive power sensitivity matrix based on load flow calculation.
Step S4, decomposing the power distribution network model by using an epsilon decomposition method, converting the power distribution network model into a plurality of weak coupling subsystem models, and solving the topological parameters of the subsystem models through a deep first search algorithm; (ii) a
Step S5, uniformly arranging the distributed photovoltaic, energy storage and power distribution network subsystem models into an overall power distribution network dynamic voltage control model;
step S6, considering the charge state, the rated capacity and the maximum active output limit of the distributed energy storage and the maximum reactive output limit of the distributed photovoltaic, and expressing the charge state, the rated capacity and the maximum active output limit as an inequality constraint form;
and step S7, calculating a real-time power distribution network dynamic voltage control instruction based on a distributed model prediction control method according to the power distribution network dynamic voltage control model.
In particular, in the implementation case of the distribution network dynamic voltage control method based on the distributed MPC,
the distributed photovoltaic dynamic mathematical model in the step 1 is as follows:
the dynamic model of the active power output of the distributed photovoltaic MPPT is
Figure BDA0002748392560000061
Wherein, TpvIs the photovoltaic time constant, PPVTo output active power for the photovoltaic inverter,
Figure BDA0002748392560000062
representing its differential amount, PMPPTOutputting power for photovoltaic MPPT;
the distributed photovoltaic reactive-voltage droop control dynamic model is
Figure BDA0002748392560000063
Wherein, tau1Is a filter constant, QPV,inThe photovoltaic inverter inputs the reactive power,
Figure BDA0002748392560000064
representing its differential amount, QoFor reactive power reference commands, KdIs a reactive-voltage droop coefficient, ViTo grid point voltage, VrefIs a grid-connected point voltage reference value;
the distributed photovoltaic inverter reactive power output dynamic model is
Figure BDA0002748392560000071
Wherein Q isiTo output the reactive power for the inverter,
Figure BDA0002748392560000072
represents the differential amount thereof;
discretizing and writing the model into a state space equation form: x is the number ofPV(k+1)＝APVxPV(k)+BPVuPV(k)+BdPVdPV(k) Wherein x isPV，dPV，uPVFor the state, disturbance and control vectors of distributed photovoltaics, APV，BPV，BdPVIs a distributed photovoltaic system matrix.
The distributed energy storage dynamic mathematical model in the step 2 is as follows:
the distributed energy storage active power output dynamic model comprises the following steps:
Figure BDA0002748392560000073
wherein, tau2pFor storing the active filter constant, PPIThe active power is input to the PI controller,
Figure BDA0002748392560000074
is PPIDifferential amount, PBESSFor energy-storage converter outputActual active power, PBESSrefA reference instruction of active power of energy storage;
Figure BDA0002748392560000075
wherein k isp、kiAs a parameter of the PI controller, PBESS，inAn active command is input to the energy storage converter,
Figure BDA0002748392560000076
representing its differential amount, PPIThe active power is input to the PI controller,
Figure BDA0002748392560000077
represents the differential amount thereof;
Figure BDA0002748392560000078
wherein, TBESSFor the time constant of the energy storage converter,
Figure BDA0002748392560000079
and outputting differential quantity of actual active power for the energy storage converter.
The distributed energy storage reactive power output dynamic model comprises the following steps:
Figure BDA00027483925600000710
wherein, tau2pFor the constant, Q, of the energy-storing reactive filterPIThe reactive power is input for the PI-controller,
Figure BDA00027483925600000711
inputting differential amounts of reactive power, Q, for PI controllersBESSFor outputting actual reactive power, Q, of the energy-storing converterBESSrefA reference instruction for energy storage reactive power;
Figure BDA00027483925600000712
wherein k isp、kiFor PI controller parameters, QBESS，inA reactive instruction is input for the energy storage converter,
Figure BDA00027483925600000713
representing its differential amount, QPIThe reactive power is input for the PI-controller,
Figure BDA00027483925600000714
represents the differential amount thereof;
Figure BDA00027483925600000715
wherein, TBESSFor the time constant of the energy storage converter,
Figure BDA00027483925600000716
outputting differential quantity of actual reactive power for the energy storage converter;
an energy storage state of charge model:
Figure BDA00027483925600000717
wherein, SOC (k) and SOC (k +1) are energy storage SOC values at sampling time k and k +1, and PBESS(k) For the actual output of active power, T, of the stored energy at sampling instant ksTo sample time, EmaxThe maximum capacity for energy storage;
discretizing and writing the distributed energy storage dynamic mathematical model into a state space equation form: x is the number ofES(k+1)＝AESxES(k)+BESuES(k) Wherein x isES，uESFor distributed energy storage states and control vectors, AES，BESIs a distributed energy storage system matrix.
The voltage-active reactive power sensitivity matrix of the power distribution network in the step 3 is as follows:
Figure BDA0002748392560000081
wherein ΛθP、ΛθQ、ΛVP、ΛVQRepresenting the voltage sensitivity coefficient.
The subsystem model in the step 4 is as follows:
ΛVP＝Λ′VP+ε·R (2)
wherein, Λ'VPThe elements being greater than the value of epsilonAnd the sensitivity matrix describes the strong coupling relation of the system, and the epsilon & R is a residual matrix describing the weak coupling of the system. All element values in R are less than or equal to 1. In addition to quantitatively describing the coupling between the distributed power sources and the nodes, the range of influence of each distributed power source is described as well as a new network topology that ignores weak couplings. For matrix Λ'VPOne permutation matrix P would be derived of'VPIs converted into
Figure BDA0002748392560000082
Wherein
Figure BDA0002748392560000083
Is a block diagonal matrix. In that
Figure BDA0002748392560000084
Each blocking matrix represents the sensitivity relationship of each subsystem. By passing
Figure BDA0002748392560000085
The described subnet topology is conveniently obtained by using a depth first search algorithm.
The power distribution network integral model in the step 5 is as follows:
Figure BDA0002748392560000086
in the formula, xi，ui，yiAre respectively a distribution network subsystem SiThe state quantity, the control input quantity and the output quantity, xjFor the sub-system S of the distribution networkjAmount of state of (A)i、Bi、DiFor the sub-system S of the distribution networkiSystem matrix of AijFor the sub-system S of the distribution networkiAnd SjSystem coupling matrix of, NiThe number of subsystems of the power distribution network. If the matrix A isijIf not, then the sub-system and S are representediIs SjCoupled, the two are adjacent systems.
The inequality constraint in the step 6 is as follows:
Figure BDA0002748392560000087
Figure BDA0002748392560000088
Figure BDA0002748392560000089
in the formula, (4) energy storage active output constraint, (5) energy storage reactive output constraint, and (6) photovoltaic reactive output constraint.
And considering the energy storage SOC and the voltage deviation regulation effect, and limiting the control by taking the energy storage SOC and the voltage deviation regulation effect as output constraints:
ΔVmin≤ΔV≤ΔVmax (7)
SOCmin≤SOC≤SOCmax (8)
in the formula, (7) is node voltage deviation constraint, and (8) is energy storage SOC constraint.
Wherein the content of the first and second substances,
Figure BDA0002748392560000091
for the minimum active output value of the stored energy,
Figure BDA0002748392560000092
the maximum active output value is the stored energy;
Figure BDA0002748392560000093
is the minimum idle work output value of the stored energy,
Figure BDA0002748392560000094
the maximum reactive power output value is the stored energy; SOCminTo store the lower limit of the energy SOC, SOCmaxIs the upper limit of the energy storage SOC;
Figure BDA0002748392560000095
is the photovoltaic minimum reactive power output value,
Figure BDA0002748392560000096
the maximum photovoltaic reactive power output value is obtained; Δ VminIs the lower limit of the voltage deviation, Δ VmaxIs the upper limit of the voltage deviation.
The calculation method of the real-time power distribution network dynamic voltage control instruction in the step 7 is as follows:
the future dynamics of the system can be predicted based on the model (3) according to the predictive control philosophy. For this purpose, the system prediction time domain is set to NpControl time domain as NmAnd N ism≤Np. At the current time k, Δ x (k) ═ x (k) — x (k-1) may be calculated and used as a starting point for predicting the future dynamics of the system, and the system state can be predicted from (3) as follows:
Figure BDA0002748392560000097
in the formula (I), the compound is shown in the specification,
Figure BDA0002748392560000098
Figure BDA0002748392560000099
Figure BDA00027483925600000910
Figure BDA00027483925600000911
Figure BDA00027483925600000912
and (3) converting the inequality constraint in the step 6 into the following form through a prediction equation:
Figure BDA00027483925600000913
in the formula, Su,bi，Sx,bi，Sx,bij，Sd,biTo constrain the matrix, Ymax，YminTo output a constraint vector.
The conversion of the problem to QCQP is described as follows:
Figure BDA0002748392560000101
in the formula, CuB (k +1| k) represents a constraint matrix and a vector,
Figure BDA0002748392560000102
and
Figure BDA0002748392560000103
representing a weighting matrix.
In order to further verify the accuracy of the distributed MPC-based power distribution network dynamic voltage control method, the validity of voltage control is verified in an IEEE33 node standard topology, system parameters are shown in Table 1, a controller is designed according to the method provided by the invention, and the effect of whether the method provided by the invention is applied or not is contrastingly analyzed.
TABLE 1 IEEE33 node Standard topology parameters
Node i Node j Impedance (L) Inductive reactance Node i Node j Impedance (L) Inductive reactance
1 2 0.0922 0.0470 17 18 0.7320 0.5740
2 3 0.4930 0.2511 2 19 0.1640 0.1565
3 4 0.3660 0.1864 19 20 1.5042 1.3554
4 5 0.3811 0.1941 20 21 0.4095 0.4784
5 6 0.8190 0.7070 21 22 0.7089 0.9373
6 7 0.1872 0.6188 3 23 0.4512 0.3083
7 8 0.7114 0.2351 23 24 0.8980 0.7091
8 9 1.0300 0.7400 24 25 0.8960 0.7011
9 10 1.0440 0.7400 6 26 0.2030 0.1034
10 11 0.1966 0.0650 26 27 0.2842 0.1447
11 12 0.3744 0.1238 27 28 1.0590 0.9337
12 13 1.4680 1.1550 28 29 0.8042 0.7006
13 14 0.5416 0.7129 29 30 0.5075 0.2585
14 15 0.5910 0.5260 30 31 0.9744 0.9630
15 16 0.7463 0.5450 31 32 0.3105 0.3619
16 17 1.2890 1.7210 32 33 0.3410 0.5302
As shown in fig. 2 and 3: fig. 2 shows the overall voltage distribution condition of a 33-node distribution network (16 node voltages are selected) when no control is performed, because the active power has a large influence on the voltage of each node under the voltage level of 12.66kV, when the distributed photovoltaic output is suddenly reduced, the voltage at the end node of the distribution network is out of limit, which affects the power quality of users of the distribution network and easily causes potential safety hazards. Considering that the photovoltaic and load changes of the power distribution network easily cause frequent fluctuation of the voltage of the power distribution network, after the method provided by the invention is applied, the overall voltage level of the power distribution network after photovoltaic access is improved by performing cooperative dynamic control on the distributed photovoltaic reactive power and the distributed energy storage active power, and controlling the overall voltage level as shown in figure 3. And the voltage level of the power grid is controlled to be maintained at 0.95-1.05 after the power grid is connected, and the safe and stable operation of the power distribution network under the severe fluctuation of distributed photovoltaic output is ensured.
In summary, the dynamic voltage control method for the power distribution network based on the distributed MPC, provided by the invention, has better accuracy and effectiveness on the voltage control of the power distribution network with distributed photovoltaic high-proportion access.

Claims (7)

1. A distributed MPC-based power distribution network dynamic voltage control method is characterized by comprising the following steps:
step S1, establishing a distributed photovoltaic dynamic mathematical model working in a reactive-voltage droop control mode and discretizing the model;
step S2, establishing a distributed energy storage dynamic mathematical model working in a current source type control mode and discretizing the model;
step S3, obtaining a distribution network voltage-active reactive power sensitivity matrix based on load flow calculation;
step S4, decomposing the power distribution network model by using an epsilon decomposition method, converting the power distribution network model into a plurality of weak coupling subsystem models, and solving the topological parameters of the subsystem models through a deep first search algorithm;
step S5, uniformly arranging the distributed photovoltaic, energy storage and power distribution network subsystem models into an overall power distribution network dynamic voltage control model;
step S6, considering the charge state, the rated capacity and the maximum active output limit of the distributed energy storage and the maximum reactive output limit of the distributed photovoltaic, and expressing the charge state, the rated capacity and the maximum active output limit as an inequality constraint form;
and step S7, calculating a dynamic voltage control instruction of the power distribution network in real time according to the power distribution network control model and based on a distributed model prediction control method.
2. The distributed MPC based power distribution network dynamic voltage control method of claim 1, wherein the mathematical model defined in step S1 includes:
the dynamic model of the active power output of the distributed photovoltaic MPPT is
Figure FDA0002748392550000011
Wherein, TpvIs the photovoltaic time constant, PPVTo output active power for the photovoltaic inverter,
Figure FDA0002748392550000012
representing its differential amount, PMPPTOutputting power for photovoltaic MPPT;
the distributed photovoltaic reactive-voltage droop control dynamic model is
Figure FDA0002748392550000013
Wherein, tau1Is a filter constant, QPV,inThe photovoltaic inverter inputs the reactive power,
Figure FDA0002748392550000014
representing its differential amount, QoFor reactive power reference commands, KdIs a reactive-voltage droop coefficient, ViTo grid point voltage, VrefIs a grid-connected point voltage reference value;
reactive power output dynamic model of distributed photovoltaic inverterIs formed by
Figure FDA0002748392550000015
Wherein Q isiTo output the reactive power for the inverter,
Figure FDA0002748392550000016
represents the differential amount thereof;
discretizing and writing the model into a state space equation form: x is the number ofPV(k+1)＝APVxPV(k)+BPVuPV(k)+BdPVdPV(k) Wherein x isPV，dPV，uPVFor the state, disturbance and control vectors of distributed photovoltaics, APV，BPV，BdPVIs a distributed photovoltaic system matrix.
3. The distributed MPC-based power distribution network dynamic voltage control method of claim 2, wherein the distributed energy storage dynamic mathematical model in the step S2 comprises:
the distributed energy storage active power output dynamic model comprises the following steps:
Figure FDA0002748392550000017
wherein, tau2pFor storing the active filter constant, PPIThe active power is input to the PI controller,
Figure FDA0002748392550000021
is PPIDifferential amount, PBESSFor the energy-storage converter to output the actual active power, PBESSrefA reference instruction of active power of energy storage;
Figure FDA0002748392550000022
wherein k isp、kiAs a parameter of the PI controller, PBESS，inAn active command is input to the energy storage converter,
Figure FDA0002748392550000023
representing its differential amount, PPIThe active power is input to the PI controller,
Figure FDA0002748392550000024
represents the differential amount thereof;
Figure FDA0002748392550000025
wherein, TBESSFor the time constant of the energy storage converter,
Figure FDA0002748392550000026
and outputting differential quantity of actual active power for the energy storage converter.
The distributed energy storage reactive power output dynamic model comprises the following steps:
Figure FDA0002748392550000027
wherein, tau2pFor the constant, Q, of the energy-storing reactive filterPIThe reactive power is input for the PI-controller,
Figure FDA0002748392550000028
inputting differential amounts of reactive power, Q, for PI controllersBESSFor outputting actual reactive power, Q, of the energy-storing converterBESSrefA reference instruction for energy storage reactive power;
Figure FDA0002748392550000029
wherein k isp、kiFor PI controller parameters, QBESS，inA reactive instruction is input for the energy storage converter,
Figure FDA00027483925500000210
representing its differential amount, QPIThe reactive power is input for the PI-controller,
Figure FDA00027483925500000211
represents the differential amount thereof;
Figure FDA00027483925500000212
wherein, TBESSFor the time constant of the energy storage converter,
Figure FDA00027483925500000213
outputting differential quantity of actual reactive power for the energy storage converter;
an energy storage state of charge model:
Figure FDA00027483925500000214
wherein, SOC (k) and SOC (k +1) are energy storage SOC values at sampling time k and k +1, and PBESS(k) For the actual output of active power, T, of the stored energy at sampling instant ksTo sample time, EmaxThe maximum capacity for energy storage;
discretizing and writing the distributed energy storage dynamic mathematical model into a state space equation form: x is the number ofES(k+1)＝AESxES(k)+BESuES(k) Wherein x isES，uESFor distributed energy storage states and control vectors, AES，BESIs a distributed energy storage system matrix.
4. The distributed MPC-based power distribution network dynamic voltage control method of claim 1, wherein the step S3 comprises: and obtaining an integral Jacobian matrix through load flow calculation of the power distribution network, and obtaining an inverse matrix of the Jacobian matrix to obtain a sensitivity equation of the amplitude and the phase angle of the voltage with respect to active power and reactive power to form a power distribution network model.
5. The distributed MPC-based power distribution network dynamic voltage control method of claim 1, wherein the step S4 comprises: and decomposing the power distribution network model by using an epsilon decomposition method according to matrix elements reflecting the relation between the voltage amplitude and the power sensitivity in the whole power distribution network, and converting the matrix elements into a plurality of weak coupling subsystem models.
6. The distributed MPC based power distribution network dynamic voltage control method as claimed in claim 3, wherein the step S6 comprises: store upThe active power output is constrained to
Figure FDA00027483925500000215
Wherein the content of the first and second substances,
Figure FDA00027483925500000216
for the minimum active output value of the stored energy,
Figure FDA0002748392550000031
the maximum active output value is the stored energy; the energy storage reactive power output is restricted to
Figure FDA0002748392550000032
Wherein the content of the first and second substances,
Figure FDA0002748392550000033
is the minimum idle work output value of the stored energy,
Figure FDA0002748392550000034
the maximum reactive power output value is the stored energy; energy storage SOC constraint to SOCmin≤SOC≤SOCmaxWherein, SOCminTo store the lower limit of the energy SOC, SOCmaxIs the upper limit of the energy storage SOC; photovoltaic reactive power output constraint of
Figure FDA0002748392550000035
Wherein the content of the first and second substances,
Figure FDA0002748392550000036
is the photovoltaic minimum reactive power output value,
Figure FDA0002748392550000037
the maximum photovoltaic reactive power output value is obtained; the node voltage deviation is constrained to Δ Vmin≤ΔV≤ΔVmaxWherein, Δ VminIs the lower limit of the voltage deviation, Δ VmaxIs the upper limit of the voltage deviation.
7. The distributed MPC-based power distribution network dynamic voltage control method as claimed in claim 1, wherein the specific process of step S7 is as follows: and according to the power distribution network model described by the dynamic model, converting the control problem into a secondary planning problem containing constraints by a DMPC method, and performing online optimization solution on the dynamic voltage control instruction to realize control.
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Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113054690A (en) * 2021-03-17 2021-06-29 华翔翔能科技股份有限公司 Voltage control method and system based on event triggering and electronic equipment
CN113162053A (en) * 2021-03-18 2021-07-23 南京邮电大学 Power distribution network voltage control method and system based on load transfer and energy storage regulation
CN113285488A (en) * 2021-05-26 2021-08-20 国网天津市电力公司 Hybrid energy storage coordination control method based on multi-level architecture
CN113904353A (en) * 2021-10-26 2022-01-07 东南大学 Distributed energy storage self-adaptive droop control method based on voltage sensitivity matrix
CN114024318A (en) * 2021-11-05 2022-02-08 国网青海省电力公司清洁能源发展研究院 Voltage control method and device for distributed optical storage system in power distribution network
CN114900041A (en) * 2022-04-14 2022-08-12 北京智源新能电气科技有限公司 Voltage-lifting direct-current converter staggered parallel system and control algorithm
CN116111598A (en) * 2022-11-21 2023-05-12 华北电力大学 MPC-based frequency-voltage coordination control method

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2164147A2 (en) * 2008-09-15 2010-03-17 General Electric Company Reactive power compensation in solar power system
CN105281360A (en) * 2015-09-14 2016-01-27 国家电网公司 Distributed photovoltaic automatic generating control method based on sensitivity
CN109861247A (en) * 2019-01-29 2019-06-07 南方电网科学研究院有限责任公司 A kind of feeder line grade quick voltage control system and method based on wide area measurement information

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2164147A2 (en) * 2008-09-15 2010-03-17 General Electric Company Reactive power compensation in solar power system
CN105281360A (en) * 2015-09-14 2016-01-27 国家电网公司 Distributed photovoltaic automatic generating control method based on sensitivity
CN109861247A (en) * 2019-01-29 2019-06-07 南方电网科学研究院有限责任公司 A kind of feeder line grade quick voltage control system and method based on wide area measurement information

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
李智;白恺;柳玉;李娜;张杨帆;董建明;任巍曦;: "光伏并网逆变器功率控制能力的试验研究", 华北电力技术, no. 12 *
赵涌泉;于汀;韩巍;蒲天骄;黄仁乐;穆云飞;: "交直流主动配电网多无功源协调的电压控制策略", 电力建设, no. 05 *

Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113054690A (en) * 2021-03-17 2021-06-29 华翔翔能科技股份有限公司 Voltage control method and system based on event triggering and electronic equipment
CN113162053A (en) * 2021-03-18 2021-07-23 南京邮电大学 Power distribution network voltage control method and system based on load transfer and energy storage regulation
CN113162053B (en) * 2021-03-18 2022-06-07 南京邮电大学 Power distribution network voltage control method and system based on load transfer and energy storage regulation
CN113285488A (en) * 2021-05-26 2021-08-20 国网天津市电力公司 Hybrid energy storage coordination control method based on multi-level architecture
CN113285488B (en) * 2021-05-26 2022-12-06 国网天津市电力公司 Hybrid energy storage coordination control method based on multi-level architecture
CN113904353A (en) * 2021-10-26 2022-01-07 东南大学 Distributed energy storage self-adaptive droop control method based on voltage sensitivity matrix
CN114024318A (en) * 2021-11-05 2022-02-08 国网青海省电力公司清洁能源发展研究院 Voltage control method and device for distributed optical storage system in power distribution network
CN114900041A (en) * 2022-04-14 2022-08-12 北京智源新能电气科技有限公司 Voltage-lifting direct-current converter staggered parallel system and control algorithm
CN116111598A (en) * 2022-11-21 2023-05-12 华北电力大学 MPC-based frequency-voltage coordination control method
CN116111598B (en) * 2022-11-21 2023-08-08 华北电力大学 MPC-based frequency-voltage coordination control method

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